Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility ...Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility of six machine learning(ML)algorithms,namely,back-propagation neural network,wavelet neural network,general regression neural network(GRNN),extreme learning machine,support vector machine and random forest(RF),to predict tunneling?induced settlement.Field data sets including geological conditions,shield operational parameters,and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models.Three indicators,mean absolute error,root mean absolute error,and coefficient of determination the(7?2)are used to demonstrate the performance of each computational model.The results indicated that ML algorithms have great potential to predict tunneling-induced settlement,compared with the traditional multivariate linear regression method.GRNN and RF algorithms show the best performance among six ML algorithms,which accurately recognize the evolution of tunneling-induced settlement.The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.展开更多
This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning technique...This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.展开更多
Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist...Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist gross errors under variable geological conditions. In order to improve the precision of the calculation model of cutterhead torque, dynamic operation parameters are considered and a new model is proposed. Experiment is carried out on a ~1.8 m shield machine test rig and the calculating re- sult with the new model is compared with the experimental data to verify the validity of the new model. The relative error of the new model is as low as 4% at smooth stage and is reduced to 5% at the end of trembling stage. Based on the results of the new model and the test data obtained from the 001.8 m test rig and the construction site, the inner relationships between several operation parameters and cutterhead torque are investigated and some quantitative conclusions are obtained.展开更多
Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead p...Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.展开更多
文章结合深圳地铁8号线大小梅沙区间直径6680 mm EPB/TBM双模盾构机模式转换工程,就模式转换技术问题,应用有限元模拟法及关键吊耳实时监测方法来加强模式转换过程中的过程把控,同时提供一种创新型门架在拔出螺旋输送机中的应用及整体...文章结合深圳地铁8号线大小梅沙区间直径6680 mm EPB/TBM双模盾构机模式转换工程,就模式转换技术问题,应用有限元模拟法及关键吊耳实时监测方法来加强模式转换过程中的过程把控,同时提供一种创新型门架在拔出螺旋输送机中的应用及整体换模施工步骤优化。通过对模式转换重难点的分析,提出提高模式转换施工安全和效率的建议。展开更多
基金The present work was carried out with the support of Research Program of Changsha Science and Technology Bureau(cskq 1703051)the National Natural Science Foundation of China(Grant Nos.41472244 and 51878267)+1 种基金the Industrial Technology and Development Program of Zhongjian Tunnel Construction Co.,Ltd.(17430102000417)Natural Science Foundation of Hunan Province,China(2019JJ30006).
文摘Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility of six machine learning(ML)algorithms,namely,back-propagation neural network,wavelet neural network,general regression neural network(GRNN),extreme learning machine,support vector machine and random forest(RF),to predict tunneling?induced settlement.Field data sets including geological conditions,shield operational parameters,and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models.Three indicators,mean absolute error,root mean absolute error,and coefficient of determination the(7?2)are used to demonstrate the performance of each computational model.The results indicated that ML algorithms have great potential to predict tunneling-induced settlement,compared with the traditional multivariate linear regression method.GRNN and RF algorithms show the best performance among six ML algorithms,which accurately recognize the evolution of tunneling-induced settlement.The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.
基金funded by“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338),。
文摘This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.
基金supported by the National Basic Research Program ("973"Program) of China (Grant No. 2007CB714004)
文摘Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist gross errors under variable geological conditions. In order to improve the precision of the calculation model of cutterhead torque, dynamic operation parameters are considered and a new model is proposed. Experiment is carried out on a ~1.8 m shield machine test rig and the calculating re- sult with the new model is compared with the experimental data to verify the validity of the new model. The relative error of the new model is as low as 4% at smooth stage and is reduced to 5% at the end of trembling stage. Based on the results of the new model and the test data obtained from the 001.8 m test rig and the construction site, the inner relationships between several operation parameters and cutterhead torque are investigated and some quantitative conclusions are obtained.
基金supported by the National Natural ScienceFoundation of China(Grant No.41672360)Science and Technology Commission of Shanghai Munici-pality(Grant No.17DZ1203800)Shanghai Shentong Metro Group Co.,Ltd.(Grant No.17DZ1203804).
文摘Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.
文摘文章结合深圳地铁8号线大小梅沙区间直径6680 mm EPB/TBM双模盾构机模式转换工程,就模式转换技术问题,应用有限元模拟法及关键吊耳实时监测方法来加强模式转换过程中的过程把控,同时提供一种创新型门架在拔出螺旋输送机中的应用及整体换模施工步骤优化。通过对模式转换重难点的分析,提出提高模式转换施工安全和效率的建议。
